Fake News Detection Based on Multi‐Perspective Differential Feature Fusion

In order to solve the problem that traditional fake news detection methods cannot extract the semantic information of short news well. We propose a fake news detection method based on the fusion of differential features from multi‐ perspective. Firstly, different pre‐trained models were used to extract features from different perspectives, and siamese convolutional neural networks were used to obtain text structure differential features. Meanwhile, siamese bidirectional long term memory networks were further used to obtain text sequence differential features. Both features were integrated to achieve fake news detection. The experimental results on public Chinese and English datasets show that the model used has improved on various indicators compared with the traditional fake news detection model. The integration of differential features from multiple perspectives can effectively extract the semantic information of short news and improve the detection ability of fake news.